تقرير
The power of dynamic social networks to predict individuals' mental health
| العنوان: | The power of dynamic social networks to predict individuals' mental health |
|---|---|
| المؤلفون: | Liu, Shikang, Hachen, David, Lizardo, Omar, Poellabauer, Christian, Striegel, Aaron, Milenkovic, Tijana |
| سنة النشر: | 2019 |
| المجموعة: | Computer Science Statistics |
| مصطلحات موضوعية: | Computer Science - Social and Information Networks, Computer Science - Machine Learning, Statistics - Machine Learning |
| الوصف: | Precision medicine has received attention both in and outside the clinic. We focus on the latter, by exploiting the relationship between individuals' social interactions and their mental health to develop a predictive model of one's likelihood to be depressed or anxious from rich dynamic social network data. To our knowledge, we are the first to do this. Existing studies differ from our work in at least one aspect: they do not model social interaction data as a network; they do so but analyze static network data; they examine "correlation" between social networks and health but without developing a predictive model; or they study other individual traits but not mental health. In a systematic and comprehensive evaluation, we show that our predictive model that uses dynamic social network data is superior to its static network as well as non-network equivalents when run on the same data. |
| نوع الوثيقة: | Working Paper |
| الوصول الحر: | http://arxiv.org/abs/1908.02614Test |
| رقم الانضمام: | edsarx.1908.02614 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |